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Forecasting Time Series Data with Facebook Prophet

You're reading from   Forecasting Time Series Data with Facebook Prophet Build, improve, and optimize time series forecasting models using the advanced forecasting tool

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Product type Paperback
Published in Mar 2021
Publisher Packt
ISBN-13 9781800568532
Length 270 pages
Edition 1st Edition
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Author (1):
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Greg Rafferty Greg Rafferty
Author Profile Icon Greg Rafferty
Greg Rafferty
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Table of Contents (18) Chapters Close

Preface 1. Section 1: Getting Started
2. Chapter 1: The History and Development of Time Series Forecasting FREE CHAPTER 3. Chapter 2: Getting Started with Facebook Prophet 4. Section 2: Seasonality, Tuning, and Advanced Features
5. Chapter 3: Non-Daily Data 6. Chapter 4: Seasonality 7. Chapter 5: Holidays 8. Chapter 6: Growth Modes 9. Chapter 7: Trend Changepoints 10. Chapter 8: Additional Regressors 11. Chapter 9: Outliers and Special Events 12. Chapter 10: Uncertainty Intervals 13. Section 3: Diagnostics and Evaluation
14. Chapter 11: Cross-Validation 15. Chapter 12: Performance Metrics 16. Chapter 13: Productionalizing Prophet 17. Other Books You May Enjoy

Modeling uncertainty in trends

You may have noticed in different component plots throughout this book that the trend shows uncertainty bounds, while the seasonality curves do not. By default, Prophet only estimates uncertainty in the trend, plus uncertainty due to random noise in the data. The noise is modeled as a normal distribution around the trend and trend uncertainty is modeled with maximum a posteriori (MAP) estimation.

MAP estimation is an optimization problem that is solved with Monte Carlo simulations. Named after the famous casino in Monaco, the Monte Carlo method uses repeated random sampling to estimate an unknown value, usually used when closed-form equations are either non-existent or computationally difficult.

In Chapter 5, Holidays, we talked about prior distributions, or the probability distribution of an estimate prior to receiving additional information about it. In MAP estimation, you are estimating the central tendency of a posterior distribution, or a probability...

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